Social and Behavioral Sciences Commons^™

Appearance Controls Interpretation Of Orientation Flows For 3d Shape Estimation, Steven A. Cholewiak, Romain Vergne, Benjamin Kunsberg, Steven W. Zucker, Roland W. Fleming

MODVIS Workshop

The visual system can infer 3D shape from orientation flows arising from both texture and shading patterns. However, these two types of flows provide fundamentally different information about surface structure. Texture flows, when derived from distinct elements, mainly signal first-order features (surface slant), whereas shading flow orientations primarily relate to second-order surface properties (the change in surface slant).

The source of an image's structure is inherently ambiguous, it is therefore crucial for the brain to identify whether flow patterns originate from texture or shading to correctly infer shape from a 2D image. One possible approach would be to use 'surface …

Can Computational Models Of Shape Explain Object Perception?, Sp Arun, Rt Pramod

MODVIS Workshop

Despite advances in computation and machine learning, computers are still far behind humans in vision. This is most likely because humans use a sophisticated object representation which is very different from that used in computers today. Another challenge is that object representations in computer vision and human vision have not been systematically compared on the same objects. To address this issue, we measured perceptual dissimilarity between objects in humans in a visual search (taking search difficulty as an index of target-distracter similarity). We then compared these observed dissimilarities against the dissimilarity predicted by a large number of state-of-the-art computational models …

Object Recognition And Visual Search With A Physiologically Grounded Model Of Visual Attention, Frederik Beuth, Fred H. Hamker

MODVIS Workshop

Visual attention models can explain a rich set of physiological data (Reynolds & Heeger, 2009, Neuron), but can rarely link these findings to real-world tasks. Here, we would like to narrow this gap with a novel, physiologically grounded model of visual attention by demonstrating its objects recognition abilities in noisy scenes.

To base the model on physiological data, we used a recently developed microcircuit model of visual attention (Beuth & Hamker, in revision, Vision Res) which explains a large set of attention experiments, e.g. biased competition, modulation of contrast response functions, tuning curves, and surround suppression. Objects are represented by …